{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.8.3\n"
     ]
    }
   ],
   "source": [
    "# Matplotlib安装\n",
    "import matplotlib\n",
    "\n",
    "print(matplotlib.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pyplot模块\n",
    "# 在图表中从位置(0, 0) 到位置(6, 250) 绘制一条直线\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "xpoints = np.array([0, 6])\n",
    "ypoints = np.array([0, 250])\n",
    "\n",
    "plt.plot(xpoints, ypoints)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}